package com.atguigu.day05;

import com.atguigu.bean.WaterSensor;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.util.ArrayList;

public class Flink07_TimeWindow_Tumbling_ProcessFun {
    public static void main(String[] args) throws Exception {
        //1.获取流的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//        StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(new Configuration());
        env.setParallelism(1);

        //2.从端口获取数据
        DataStreamSource<String> streamSource = env.socketTextStream("localhost", 9999);

        //3.把数据转为JSON字符串
        SingleOutputStreamOperator<WaterSensor> waterSensorJsonStream = streamSource.map(new MapFunction<String, WaterSensor>() {
            @Override
            public WaterSensor map(String value) throws Exception {
//                System.out.println("数据进来的时间："+System.currentTimeMillis()/1000);
                String[] split = value.split(",");
                WaterSensor waterSensor = new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
                return waterSensor;
            }
        });
        //4.将相同的id聚合到一块
        KeyedStream<WaterSensor, Tuple> keyedStream = waterSensorJsonStream.keyBy("id");

        //5.开启一个基于处理时间的滚动窗口 因为是在keyby之后开的窗，所以窗口是分key的
        WindowedStream<WaterSensor, Tuple, TimeWindow> window = keyedStream.window(TumblingProcessingTimeWindows.of(Time.seconds(5)));

        //TODO 6.对窗口中的数据做Sum累加计算用ProcessFun实现（全窗口函数）
        window.process(new ProcessWindowFunction<WaterSensor, Tuple2<String, Integer>, Tuple, TimeWindow>() {
            private Integer lastSum = 0;

            @Override
            public void process(Tuple tuple, Context context, Iterable<WaterSensor> elements, Collector<Tuple2<String, Integer>> out) throws Exception {
                System.out.println("process...");
//                ArrayList<WaterSensor> list = new ArrayList<>();
                for (WaterSensor element : elements) {
//                    list.add(element);
                    lastSum = lastSum + element.getVc();
                }
//                out.collect(Tuple2.of(tuple.toString(), list.size()));
                out.collect(Tuple2.of(tuple.toString(), lastSum));
            }
        }).print();

        env.execute();


    }
}
